Analysis of pedestrian congestion

ABSTRACT

A method, system and computer program product are disclosed for providing a measure of pedestrian congestion in a given area. In one embodiment, the method comprises collecting data from a pedestrian moving in the area, and using the data to determine one or more specified parameters representing a pattern of movement of the pedestrian in the area. These specified parameters are used to determine a sinuosity of pedestrian traffic in the given area. In one embodiment, the pattern of movement is a walking pattern of the pedestrian, and the data collected from the pedestrian identifies a multitude of positions of the pedestrian&#39;s feet. In an embodiment, the specified parameters include a lateral gap between the feet of the pedestrian, and a length of a stride of the pedestrian. A sequence of values may be collected for each of these parameters.

BACKGROUND

This invention generally relates analyzing pedestrian congestion, andmore specifically to providing one or more measures of pedestriancongestion.

More and more, smart transportation systems integrate the “transportmulti-modality” where a commuter can use multiple transportation meanssuch as cars, bicycles, busses, trains and walking. Walking is veryimportant because people can almost never assume that all the othertransportation means will bring a person to the final destinationwithout having to do at least some walking. Understanding the walkingconditions, or the “fluidity” of walking, is important so that personaltransportation journey advisors can guide people precisely andefficiently.

The time needed for a given pedestrian P to walk from point A to point Bdepends on three factors: the distance between the two points A and B;the walking characteristics of the pedestrian P; and the fluidity of thepedestrian traffic. The walking characteristics of the pedestrian mainlyrelate to his or her regular walking speed v. The fluidity of thepedestrian traffic is a measure of the amount of obstacles (such asother pedestrians) that prevent a person from freely walking ormarching.

BRIEF SUMMARY

Embodiments of the invention provide a method, system and computerprogram product for providing a measure of pedestrian congestion in agiven area. In one embodiment, the method comprises collecting data froma pedestrian moving in said given area, and using said data to determineone or more specified parameters representing a pattern of movement ofthe pedestrian in the given area. The one or more specified parametersare used to determine a sinuosity of pedestrian traffic in the givenarea.

In one embodiment, the pattern of movement is a walking pattern of thepedestrian, and the data collected from the pedestrian identifies amultitude of positions of the feet of the pedestrian.

In an embodiment, the one or more specified parameters include oneparameter, e, representing a lateral gap between the feet of thepedestrian; and another parameter, l, representing a length of a strideof the pedestrian.

In one embodiment, a first series of values {e_(i)} is collected for e,and a standard deviation of these first series of values is determined.

In an embodiment, a second series of values {l_(i)} is collected for l.

In one embodiment, this second series of values is used to calculate anaverage stride length for the pedestrian.

In an embodiment, one or more samples of the second series of values areused to calculate this average stride length; and the standard deviationof the first series of values is used to determine said one or moresamples of the second series of values.

In one embodiment, a stride and gap series {√{square root over(l_(i)+e_(i))}} is computed and used to determine a defined variablerepresenting movement of the pedestrian in the given area.

In one embodiment, the sinuosity of pedestrian traffic is determined byusing the standard deviation of the first series of values.

In an embodiment, the sinuosity, s, is determined as a function of saidstandard deviation σ(s) using the equation:

${\sigma(s)} = {\frac{1}{d\mspace{11mu} s^{2}}\left\lbrack {{1\sqrt{s^{2} - 1}} + {e\mspace{11mu}\left( {1 - s} \right)}} \right\rbrack}$where d is a distance between defined obstacles in the walking path ofthe pedestrian.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a diagram of a path taken by a pedestrian moving along astraight line.

FIG. 2 is a diagram of a pedestrian path that is not a straight line.

FIG. 3 shows transducers mounted on the feet of a person in order tomeasure distance between those feet.

FIG. 4 is a graph showing the distance between the feet of a person asthe person is walking.

FIG. 5 show various feet positions and distances as a person walks alonga path.

FIG. 6 shows a path taken by a pedestrian to avoid several obstacles.

FIG. 7 is a chart showing the standard deviation of a gap in apedestrian's stride as a function of the distance between twoconsecutive obstacles.

FIG. 8 is a chart showing the sinuosity of pedestrian traffic as afunction of the standard deviation shown in the chart of FIG. 7.

FIG. 9 depicts a group of meters used to monitor pedestrian congestion.

FIG. 10 is a flow chart illustrating an operation of an embodiment ofthe invention.

FIG. 11 shows a computing environment in which embodiments of theinvention may be implemented.

FIG. 12 illustrates a computer system that may be used as a server orclient in the environment shown in FIG. 11.

DETAILED DESCRIPTION

As will be appreciated by one skilled in the art, embodiments of thepresent invention may be embodied as a system, method or computerprogram product. Accordingly, embodiments of the present invention maytake the form of an entirely hardware embodiment, an entirely softwareembodiment (including firmware, resident software, micro-code, etc.) oran embodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, embodiments of the present invention may take the form of acomputer program product embodied in any tangible medium of expressionhaving computer usable program code embodied in the medium.

Any combination of one or more computer usable or computer readablemedium(s) may be utilized. The computer-usable or computer-readablemedium may be, for example but not limited to, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,device, or propagation medium. More specific examples (a non-exhaustivelist) of the computer-readable medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CDROM), an optical storage device, a transmission media such as thosesupporting the Internet or an intranet, or a magnetic storage device.Note that the computer-usable or computer-readable medium could even bepaper or another suitable medium, upon which the program is printed, asthe program can be electronically captured, via, for instance, opticalscanning of the paper or other medium, then compiled, interpreted, orotherwise processed in a suitable manner, if necessary, and then storedin a computer memory. In the context of this document, a computer-usableor computer-readable medium may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.The computer-usable medium may include a propagated data signal with thecomputer-usable program code embodied therewith, either in baseband oras part of a carrier wave. The computer usable program code may betransmitted using any appropriate medium, including but not limited towireless, wireline, optical fiber cable, RF, etc.

Computer program code for carrying out operations of the presentinvention may be written in any combination of one or more programminglanguages, including an object oriented programming language such asJava, Smalltalk, C++ or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork, including a local area network (LAN) or a wide area network(WAN), or the connection may be made to an external computer (forexample, through the Internet using an Internet Service Provider).

The present invention is described below with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products according to embodiments of the invention. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerprogram instructions. These computer program instructions may beprovided to a processor of a general purpose computer, special purposecomputer, or other programmable data processing apparatus to produce amachine, such that the instructions, which execute via the processor ofthe computer or other programmable data processing apparatus, createmeans for implementing the functions/acts specified in the flowchartand/or block diagram block or blocks. These computer programinstructions may also be stored in a computer-readable medium that candirect a computer or other programmable data processing apparatus tofunction in a particular manner, such that the instructions stored inthe computer-readable medium produce an article of manufacture includinginstruction means which implement the function/act specified in theflowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide processes for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

The present invention relates to analyze pedestrian congestion in agiven area. The analysis can be used to advise people on the pedestriantraffic in that area and to help guide people who are walking throughthe area.

As mentioned above, the time needed for a given pedestrian P to walkfrom point A to point B depends on three factors: the distance betweenthe two points A and B; the walking characteristics of the pedestrian P;and the fluidity of the pedestrian traffic. The walking characteristicsof the pedestrian mainly relate to his or her regular walking speed v.The fluidity of the pedestrian traffic is a measure of the amount ofobstacles (such as other pedestrians) that prevent a person from freelywalking or marching.

The latter parameter is referred to as the “sinuosity,” noted as “s,” ofthe ground, and is measured as the ratio of the distance a personactually walks between points A and B to the geometric, or straightline, distance between those two points. Without any obstacles in thepedestrian's path, s=1. With obstacles in the path, s>1.

The time, t, needed by the pedestrian to walk from the point A to thepoint B, is determined as:

$t = {\frac{d\left( {A,B} \right)}{v} \times s}$

d(A,B) is known from A and B;

s is determined, as discussed in detail below; and

v is associated with the pedestrian.

Embodiments of the invention provide a model driven by the successivepositions of the right and left feet of the pedestrian.

FIG. 1 shows a path 100 of a pedestrian moving along a straight line.Line 102 represents the path of the pedestrian's right foot, and line104 represents the path of the pedestrian's left foot. In this Fig.,

-   -   D_(i) represents successive positions of the right foot on the        ground;    -   G_(i) represents successive positions of the left foot on the        ground;    -   e and l are parameters characterizing the walking pattern of the        pedestrian; e is the lateral distance, referred to as the “gap,”        between the line of the right foot and the line of the left foot        at various points along the path 100; l is the longitudinal        distance, referred to as the “stride,” between successive        positions of the right and left feet—that is, between a position        of one of the feet and the next position of the other foot, for        example between positions G₋₂ and D₋₁.

If there are no obstacles in the way, the pedestrian walks in arelatively constant, straight way, so the parameters e and l remainalmost constant. This means that, typically, the standard deviations ofthe series of gap values {e_(i)} and the series of stride values {l_(i)}remain quite low, although not zero.

FIG. 2 shows a path 200 taken by a pedestrian when he or she does notwalk in a straight line. This path walked by the pedestrian may bereferred to as a broken line. In FIG. 2, line 202 represents the line ofthe pedestrian's right foot, and line 204 represents the line of thepedestrian's left foot.

In the example of FIG. 2, e, as measured just forward of G₋₃ and justforward of G₁, is constant, and l is also constant. The gap between thelines of the left and right feet increases slightly at point D₀. Thelength of this gap, dd, can be determined from trigonometry as afunction of the angle, α, between line segments 204 a and 202 b.

${dd} = {{1\sin\frac{(\alpha)}{2}} + {e\;\cos\frac{(\alpha)}{2}}}$

-   -   If α=o, then dd=e: which means lines 202 and 204 are straight,        as in FIG. 1;    -   If α>0, and α remains small,

${{dd} \cong {e + {1\frac{(\alpha)}{2}}}};$

-   -    and    -   dd is slightly greater than e;    -   If α<0 and α remains small,

${{dd} \cong {e - {1\frac{(\alpha)}{2}}}};$

-   -    and    -   dd is slightly less than e.

Aspects of a walking model are discussed below with reference to FIGS.3-5.

Embodiments of the invention use shoe mounted transceivers to measuremovement of a person's feet. With reference to FIG. 3, in oneembodiment, each shoe is equipped with a transceiver: one of thesetransceivers is a primary transceiver 302, and the other is a secondarytransceiver 304. To measure foot movement and related distances, theprimary transceiver transmits a train of pulses at frequency F1, with arate R1, starting a timer count at each pulse. As a typical example,R1=10 Hz.

The secondary transceiver, upon reception of a pulse from the primarytransceiver at frequency F1, waits for a fixed, very short delay t (forexample, 0.1 us) and then transmits a pulse at a frequency F2. Uponreception of a pulse F2, the primary transceiver records the timer countT and then waits for the next R1 pulse to restart the timer count.

There is a relationship between T and the distance d between the feet:T=Δt+d/s

In this embodiment, the primary transceiver records the distance d in atable.

FIG. 4 is a chart showing one example of the changing distances betweenthe feet of a person as that person walks. The maximum distance betweenthe feet usually occurs when one foot that had been moving comes to reston the ground, ahead of the other foot. The minimum distance between thefeet usually occurs when the moving foot is immediately to the side ofthe stationary foot.

The “gap” serie {e_(i)} corresponds to the minimas of the foot distances402. The “gap & stride” series corresponds to the maximas of the footdistances 404.

The primary transceiver 302 determines the successive maximas andminimas from the recorded foot distance samples (possibly withinterpolation). For each triplet of successive Min-Max-Min distancevalues, the primary receiver computes the corresponding walkingdistance, and related distances, using trigonometry.

FIG. 5 shows four of the feet positions of a walking person. At time t0,the right foot of the person is stationary on the ground at Da, and theleft foot is at Ga and is moving past the right foot. At time t1, bothfeet are stationary on the ground, with the right foot at Da and theleft foot at Gb. At this point, the person begins to move the rightfoot; and at time t2, the right foot is at Db, directly to the side ofthe left foot.

As represented in FIG. 5, the length of the person's stride is l, andthe distance walked from t0 to t2 is distance (Pa Pb). The person's footgap at t0 is da, and the foot gap at t2 is db, and, as can be seen, thisfoot gap increases from t0 to t2.

At t0, the distance between the feet is:DaGa, d(t0)=√{square root over (da ² +k ²)},where k is the elevation of the left foot above the ground.

At time t1, the distance between the feet is:DaGb, d(t1)=√{square root over (l ² +db ²)}

At time t2, the distance between the feet is:DbGb, d(t2)=√{square root over (db ² +k ²)}

The distance, w, walked by the person from t0 to t2 is:

$W = {{d\left( {{Pa},{Pb}} \right)} = \sqrt{\frac{d_{1}^{2} - d_{2}^{2} + k^{2} + \left( {\sqrt{d_{0}^{2} - k^{2}} - \sqrt{d_{2}^{2} - k^{2}}} \right)^{2}}{4}}}$

FIG. 6 shows a path 602 of a pedestrian as he or she walks past severalobstacles in a given area. A number of parameters are shown in thisFig., including d, f, l and ψ. d is the distance between successiveobstacles. f is the free space between the pedestrian and an obstacle asthe pedestrian passes the obstacle. A typical value is 0.5 m. l is thelength of the stride of the pedestrian. A typical value is 0.75 m. ψ isthe angle between a straight line path that the pedestrian would take ifthere were no obstacles and the individual path segments 602 a, 602 b,602 c, 602 d, 602 e along which the pedestrian actually walks to avoidthe obstacles.

F, d and ψ are related as follows:tan ψ=2f/d and s=1/cos ψ.

Also, along the portion of the path designated at 604 and referred to asthe Obstacle Avoidance Pattern (OAP), the total number of strides, N,taken by the pedestrian is given by the equation:N=d/l cos ψ.

These N strides are comprised of N-1 straight strides with foot gape_(i)=e, and l curved stride with foot gap e_(i)=e cos ψ+l sin ψ.

${E\left( e_{i} \right)} = {{\frac{1\mspace{11mu}\cos\mspace{11mu}\psi}{d}\left\lbrack {{1\mspace{11mu}\sin\mspace{11mu}\psi} + {e\mspace{11mu}\left( {{\cos\mspace{11mu}\psi} - 1} \right)}} \right\rbrack} + e}$

With reference to FIGS. 7 and 8, the standard deviation of the gap e,expressed in terms of the distance between two consecutive obstacles,is:

$\sigma = {\frac{1}{sd}\left\lbrack {{1\mspace{11mu}\sin\mspace{14mu}\psi} + {e\mspace{11mu}\left( {{\cos\mspace{14mu}\psi} - 1} \right)}} \right\rbrack}$Or, expressed as a function of sinuosity, s,

${\sigma{()}} = {\frac{1}{d\mspace{11mu} s^{2}}\left\lbrack {{1\;\sqrt{s^{2} + 1}} + {e\mspace{11mu}\left( {1 - s} \right)}} \right\rbrack}$

The above-discussion modelizes the effect of pedestrian trafficcongestion by a trajectory made of a succession of broken lines foravoiding collisions with other pedestrians. This leads to a series ofgap measurements {e_(i)} with an increasing standard deviation. Theformula above gives the relation between the standard deviation and theincrease in the distance to walk between two obstacles.

When the pedestrian traffic becomes more and more congested, a secondeffect (after the one modelized above) is a decrease of the pedestrian'sstride length. As the number of strides per unit of time, referred to asthe marching frequency, is approximately constant for a given person,the decrease in the stride length has a linear effect on the time takento walk a given distance. Accordingly, embodiments of the inventionmonitor the average value of a parameter referred to as the “gap &stride” (the maxima values of the distance between feet) correspondingto √{square root over (e_(i)+l_(i))}.

In embodiments of the invention, the transceivers are used to measureover time the distance between the feet of the pedestrian and to recordthe serie of minima (the “gap” serie {e_(i)}) and the serie of maxima(the “stride & gap” serie {√{square root over (l_(i)+e_(i))}}).

An average value “L” of the stride serie {l_(i)}, for the samples wherethe standard deviation of the gap serie {e_(i)} remains below athreshold “Σ”, is determined and stored in memory. This average stridevalue “L” corresponds to the pedestrian's stride in normal trafficcondition, without congestion.

Embodiments of the invention compute, over a specified subset of therecorded series, the standard deviation σ of the gap serie {e_(i)}, theaverage value of the stride serie {l_(i)} the parameter L. These valuesL and σ may be stored in memory. In embodiments of the invention, theabove-identified values may be computed in response to a request, andthe computed values may be returned to the requestor.

With reference to FIG. 9, embodiments of the invention provide one ormore pedestrian traffic jam meters 900 that, for example, may beanchored within a smart city transportation infrastructure. Each meteris, in embodiments of the invention, configured to request and receiveinformation from pedestrian foot transceivers, as described above, andto compute, from a gap serie standard deviation σ, the traffic sinuositys according to the formula discussed above. The pedestrian traffic meteralso computes, from the average stride value divided by the referencestride L, another parameter called compression, c, using the equation:

$C = {\frac{\overset{\_}{\left\{ 1_{i} \right\}}}{L}\left( {c \leq 1} \right)}$

In embodiments of the invention, the pedestrian traffic meter alsocomputes an overall pedestrian traffic congestion slow down factor,S=s/c (≧1). S may be used as a time scaling factor T_(congestion), forevaluating the time needed by a Pedestrian to move from point A to pointB.T _(congestion) =T _(normal) ×S

Each meter 900 is uniquely identified by a meter ID and an index (e.g.A.1), and meters belonging to the same measurement point share the samemeter ID. The meters are equipped with communication means, typicallywireless ones, for broadcasting the meters signature (ID, index) througha beacon. The foot transceivers on the pedestrians continuously monitorfor the presence of meters by listening for the beacons issued by themeters. The foot transceivers also are continuously monitoring thepedestrian's stride information, as explained above, by recording aseries of stride information samples.

FIG. 10 is a flow chart showing, as an example, one embodiment of theinvention, and, more specifically, the way in which an embodiment of theinvention may be implemented in the environment shown in FIG. 9. Asillustrated in FIG. 10, after the foot transceivers are powered on, datastores in the transceivers are initialized at 1002 and 1004. At step1002, three sets of fields, referred to as container.source,container.destination, and container.stride, are set to null values. Thecontainer.source fields are used to store current data from thetransceivers 302, 304. The container.destination fields are used tostore data that is ready to be sent to one of the jam meters, andcontainer.stride fields are used to store current stride values. At step104, three sets of fields, referred to as archive.source,archive.destination and archive.stride, are set to null values. Thearchive.source fields are used to archive data collected from thetransceivers. Archive.destination fields are used to archive data sentto one of the jam meters, and archive.stride fields are used to archivestride values.

At 1006, the transceivers 302, 304 monitor for a beacon from one of thejam meters 900 and also current stride information is stored atcontainer stride. If no beacon is detected, the transceiver powers-off.

If a beacon is detected, then, at 1010, the strength of the beacon thereceived signal strength indicator (RSSI) is compared to a minimum level(RSSI min). If the beacon strength (RSSI) is not greater than thatminimum level, the process moves to step 1012, where the beacon strengthis compared to a previously stored value average (RSSI ave) to determineif the beacon strength is decreasing. If the beacon strength is notdecreasing, the process returns to the monitoring mode at step 1110. Ifthe beacon strength is decreasing, the process moves on to step 1014.The routine of FIG. 10 also moves on to step 1014 if, at step 1110, thebeacon strength is above the set minimum level.

At step 1014, the meter ID is used to determine if that ID is the sameas the last detected meter ID. If these two IDs are the same, then atstep 1016 the transceiver checks its archive fields to determine ifthose archive are set to null values. If the archive is not null, thecontents of the archive are broadcast to the jam meter, at step 1020,and the routine returns to step 1110. If the archive is null at step1016, the routine returns directly to the monitoring mode, at step 1110.

If at step 1014, the detected meter is not the same as the previousdetected meter, then, at step 1022, a destination ID and index arestored and the current stride information is stored in the containerstride fields. At step 1024, the container fields are broadcast to thedetected meter and the archive fields are set equal to the containerfields. At step 1026, the container.source field is set equal to thecontainer.destination field, the container.destination andcontainer.stride fields are set equal to null values, and the routineproceeds to the monitoring mode at step 1006.

FIG. 11 depicts a pictorial representation of an exemplary distributedcomputer system 1100 in which aspects of the illustrative embodimentsmay be implemented. Distributed system 1100 may include a network ofcomputers in which aspects of the illustrative embodiments may beimplemented. The distributed data processing system 1100 contains atleast one network 1102, which is the medium used to providecommunication links between various devices and computers connectedtogether within 1100. The network 1102 may include connections, such aswire, wireless communication links, or fiber optic cables.

In the depicted example, server 1104 and server 1106 are connected tonetwork 1102 along with storage unit 1108. In addition, clients 1110,1112, and 1114 are also connected to network 1102. These clients may be,for example, personal computers, network computers, or the like. In thedepicted example, server 1104 provides data, such as boot files,operating system images, and applications to the clients 1110, 1112, and1114. Distributed system 1100 may include additional servers, clients,and other devices not shown.

In the depicted example, the Internet, a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. The distributed system 1100 may also be implemented toinclude a number of different types of networks, such as for example, anintranet, a local area network (LAN), a wide area network (WAN), or thelike. FIG. 11 is intended as an example, not as an architecturallimitation, and the particular elements shown in FIG. 11 should not beconsidered limiting with regard to the environments in which theillustrative embodiments of the present invention may be implemented.

With reference now to FIG. 12, a block diagram of a computer or computersystem 1200 is shown. System 1200 is an example of a computer, such asserver 1104 or client 1110 in FIG. 11. In this illustrative example,system 1200 includes communications fabric 1202, which providescommunications between processor unit 1204, memory 1206, persistentstorage 1208, communications unit 1210, input/output (I/O) unit 1212,and display 1214.

Processor unit 1204 serves to execute instructions for software that maybe loaded into memory 1206. Processor unit 1204 may be a set of one ormore processors or may be a multi-processor core, depending on theparticular implementation. Memory 1206 and persistent storage 1208 areexamples of storage devices. Memory 1206, in these examples, may be arandom access memory or any other suitable volatile or non-volatilestorage device. Persistent storage 1208 may take various forms dependingon the particular implementation. For example, persistent storage 1208may be a hard drive, a flash memory, a rewritable optical disk, arewritable magnetic tape, or some combination of the above.

Communications unit 1210, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 1210 is a network interface card. Communicationsunit 1210 may provide communications through the use of either or bothphysical and wireless communications links. Input/output unit 1212allows for input and output of data with other devices that may beconnected to data processing system 1200. For example, input/output unit1212 may provide a connection for user input through a keyboard andmouse. Further, input/output unit 1212 may send output to a printer.Display 1214 provides a mechanism to display information to a user.

Those of ordinary skill in the art will appreciate that the hardware inFIGS. 11 and 12 may vary depending on the implementation. Other internalhardware or peripheral devices, such as flash memory, equivalentnon-volatile memory, or optical disk drives and the like, may be used inaddition to or in place of the hardware depicted in FIGS. 11 and 12.

What is claimed is:
 1. A method of providing a measure of pedestriancongestion in a given area, the method comprising: collecting data froma pedestrian moving in said given area; using said data to determine oneor more specified parameters representing a pattern of movement of thepedestrian in the given area; and from said one or more specifiedparameters, determining a sinuosity of pedestrian traffic in the givenarea.
 2. The method according to claim 1, wherein: said pattern ofmovement is a walking pattern of the pedestrian; and said dataidentifies distances between the feet of the pedestrian.
 3. The methodaccording to claim 2, wherein the one or more specified parametersinclude: one parameter, e, representing a lateral gap between the feetof the pedestrian; and another parameter, l, representing a length of astride of the pedestrian.
 4. The method according to claim 3, whereinthe using the data to identify one or more specified parametersincludes: collecting a first series of values {e_(i)} for said oneparameter; and determining a standard deviation of said first series ofvalues.
 5. The method according to claim 4, wherein the using the usingthe data to identify one or more specified parameters further includescollecting a second series of values {l_(i)} for said another parameter.6. The method according to claim 5, wherein the using the data toidentify one or more specified parameters further includes using thesecond series of values to calculate an average stride length for thepedestrian.
 7. The method according to claim 6, wherein the using thesecond series of values to calculate an average stride length includes:using one or more samples of the second series of values to calculatesaid average stride length; and using said standard deviation of thefirst series of values to identify said one or more samples of thesecond series of values.
 8. The method according to claim 5, wherein theusing the data to identify one or more specified parameters includes:computing a stride and gap series {l_(i)+e_(i)}; and using said strideand gap series to determine a defined variable representing movement ofthe pedestrian in the given area.
 9. The method according to claim 4,wherein the determining the sinuosity includes determining saidsinuosity by using said standard deviation of the first series ofvalues.
 10. The method according to claim 9, wherein the sinuosity, s,is determined as a function of said standard deviation using theequation:${\sigma{()}} = {\frac{1}{d\mspace{11mu} s^{2}}\left\lbrack {{1\;\sqrt{s^{2} + 1}} + {e\left( {1 - s} \right)}} \right\rbrack}$where d is a distance between defined obstacles in the walking path ofthe pedestrian.
 11. A system for providing a measure of pedestriancongestion in a given area, the system comprising: one or more sensorson a pedestrian moving in said given area to generate data from thepedestrian; and one or more processing units configured for receivingsaid data, for using said data to determine one or more specifiedparameters representing a pattern of movement of the pedestrian in thegiven area, and from said one or more specified parameters, determininga sinuosity of pedestrian traffic in the given area.
 12. The systemaccording to claim 11, wherein: said pattern of movement is a walkingpattern of the pedestrian; said data identifies a multitude of positionsof the feet of the pedestrian; and the one or more specified parametersinclude one parameter, e, representing a lateral gap between the feet ofthe pedestrian, and another parameter, l, representing a length of astride of the pedestrian.
 13. The system according to claim 12, whereinthe using the data to determine one or more specified parametersincludes: collecting a first series of values {e_(i)} for said oneparameter; collecting a second series of values {l_(i)} for said anotherparameter; and determining a standard deviation of said first series ofvalues.
 14. The system according to claim 13, wherein the using the datato determine one or more specified parameters further includes using thesecond series of values to calculate an average stride length for thepedestrian.
 15. The system according to claim 14, wherein the using thesecond series of values to calculate an average stride length includes:using one or more samples of the second series of values to calculatesaid average stride length; and using said standard deviation of thefirst series of values to identify said one or more samples of thesecond series of values.
 16. An article of manufacture comprising: atleast one tangible computer readable device having computer readableprogram code logic tangibly embodied therein to provide a measure ofpedestrian congestion in a given area based on data from a pedestrianmoving in said given area, the computer readable program code logic,when executing, performing the following: collecting said data; usingsaid data to determine one or more specified parameters representing apattern of movement of the pedestrian in the given area; and from saidspecified parameters, determining a sinuosity of pedestrian traffic inthe given area.
 17. The article of manufacture according to claim 16,wherein: said pattern of movement is a walking pattern of thepedestrian; and said data identifies a multitude of positions of thefeet of the pedestrian.
 18. The article of manufacture according toclaim 17, wherein: the one or more specified parameters include oneparameter, e, representing a lateral gap between the feet of thepedestrian, and another parameter, l, representing a length of a strideof the pedestrian; and the using the data to determine one or morespecified parameters includes collecting a first series of values{e_(i)} for said one parameter, collecting a second series of values{l_(i)} for said another parameter, and determining a standard deviationof said first series of values.
 19. The article of manufacture accordingto claim 18, wherein the using the data to determine one or morespecified parameters further includes: computing a stride and gap series{√{square root over (l_(i)+e_(i))}}; and using said stride and gapseries to determine a defined variable representing movement of thepedestrian in the given area.
 20. The article of manufacture accordingto claim 18, wherein the determining the sinuosity includes determiningsaid sinuosity by using said standard deviation of the first series ofvalues.